Results on ActivityNet-v1.3.
In Table REF  and Table REF , we compare the proposed BCNet with other methods on ActivityNet-v1.3.
For the temporal action proposal generation task, as shown in Table REF , the performance of BCNet again outperforms state-of-the-art proposal generation methods in terms of AR@AN with AN varying from 1 to 100 and AUC.
Especially when AN equals 1, we achieve 35.2% regarding the AR metric, which indicates that top-1 proposal has high quality.
For the temporal action detection task, as summarized in Table REF ,
our method achieves notable improvements on mAP over other proposal generation methods such as BMN [1]} and G-TAD [2]} at all tIoU thresholds.
When tIoU is 0.95, the mAP we obtain is 10.6%, indicating that the confidence of the generated proposals are more reliable.
